Systems and methods for a configurable device environment

ABSTRACT

A system for a configurable device environment, the system comprising a computing device configured to receive remote data corresponding to a subject and a plurality of signals from at least a sensor proximate to the subject, retrieve a biometric profile of the subject, identify a pattern of accessory device states for a plurality of accessory devices, wherein identifying includes determining a coordinated state change for a group of accessory devices of the plurality of accessory devices as a function of the remote data and the biometric profile and identifying the pattern of accessory device states as a function of the coordinated state change, determine an automation rule for the group of accessory devices as a function of the pattern of accessory device states, and transmit, to the group of accessory devices, the automation rule.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Non-provisionalapplication Ser. No. 17/087,713 filed on Nov. 3, 2020 and entitled“SYSTEMS AND METHODS FOR A CONFIGURABLE DEVICE ENVIRONMENT,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of electronicdevice configuration. In particular, the present invention is directedto systems and methods for a configurable device environment.

BACKGROUND

Electronic devices are becoming increasingly popular in a range ofapplications. Device management for configuring an environment in theabsence of user feedback is difficult to maintain, especially for arange of electronically controllable devices such as thermostats,lighting devices, household appliances, etc., that typically operatewithout direct communication.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for a configurable device environment, the systemincluding a computing device configured to receive remote datacorresponding to a subject, wherein receiving further includes receivinginteraction data from a plurality of accessory devices; and receiving aplurality of signals from at least a sensor proximate to the subject,wherein a signal of the plurality of signals includes skin data;retrieve a biometric profile of the subject as a function of theplurality of signals; identify a pattern of accessory device states forthe plurality of accessory devices, wherein identifying the patternfurther includes determining, by the computing device, a coordinatedstate change for a group of accessory devices of the plurality ofaccessory devices as a function of the interaction data and thebiometric profile; and identifying the pattern of accessory devicestates as a function of the coordinated state change; and control thefunction of the plurality of accessory devices as a function of thepattern of accessory device states.

In another aspect, a method for a configurable device environment, themethod comprising a computing device configured for receiving, by atleast a computing device, remote data corresponding to a subject,wherein receiving further includes receiving interaction data from aplurality of accessory devices; and receiving a plurality of signalsfrom at least a sensor proximate to the subject, wherein a signal of theplurality of signals includes skin data; retrieving, by the at leastcomputing device, a biometric profile of the subject as a function ofthe plurality of signals; identifying, by the at least computing device,a pattern of accessory device states for the plurality of accessorydevices, wherein identifying the pattern further includes determining,by the computing device, a coordinated state change for a group ofaccessory devices of the plurality of accessory devices as a function ofthe interaction data and the biometric profile; and identifying thepattern of accessory device states as a function of the coordinatedstate change; and controlling, by the at least computing device, thefunction of the plurality of accessory devices as a function of thepattern of accessory device states.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating a system for a configurabledevice environment;

FIG. 2 is a block diagram illustrating a non-limiting exemplaryembodiment of a machine-learning module;

FIG. 3 is a block diagram illustrating a non-limiting exemplaryembodiment of a device configuration database;

FIG. 4 is a diagrammatic representation illustrating a non-limitingexemplary embodiment of a biometric profile;

FIG. 5 is a diagrammatic representation illustrating a non-limitingexemplary embodiment of an automation rule transmitted by a computingdevice;

FIG. 6 is a block diagram of an exemplary embodiment of a workflow of amethod for a configurable device environment; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for a configurable device environment. In anembodiment, the system includes a computing device configured to receiveremote data from a plurality of accessory devices that corresponds tohow the devices interact with a subject, and may include a plurality ofsignals from sensors gathering data about the subject. The computingdevice is configured to receive a biometric profile of the subject.Computing device may be configured to generate the biometric profile ofthe subject by training machine-learning models with data correspond tosignals transmitted from devices as a function of the interaction of thedevice with the subject. The system may use the biometric profile toidentify a change in the accessory device states for the plurality ofaccessory devices to guide the accessory devices to produce an optimumenvironment for the subject. The system may transmit an automation ruleto the accessory device to automatedly alter the states of the accessorydevices to achieve and maintain an optimum environment for the subject.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 for aconfigurable device environment is illustrated. System 100 includes acomputing device 104. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

Continuing in reference to FIG. 1 , computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1 , computing device 104 is configuredto receive remote data corresponding to a subject. As used in thisdisclosure, “remote data,” is data received from a remote device such asan accessory device and/or a sensor as described in this disclosure;remote data may include interaction data and/or any data correspondingto a subject's interaction with an accessory device. An “accessorydevice,” as used in this disclosure, is any device or thing located inan environment that is controllable (at least to some degree) by acontroller. An accessory device 108 may include devices that are able tohave an effect on the subject's environment. An accessory device 108 mayinclude a computing device such as a “smartphone”, television, laptop,tablet computer, internet-of-things (IOT) device, and the like. Anaccessory device 108 may include household appliances, windows, garagedoor system, vehicle lock, light fixture, security camera, sprinklersystem, home entertainment systems, thermostat, humidifier, airpurifier, ambient sound generator, television, stereo, and the like,that may have an effect on the subject's environment. An accessorydevice 108 may have an effect on the subject's environment by alteringand/or controlling the humidity, temperature, sound level, lighting,pollutants, allergens, atmosphere, access, control, and the like.

Continuing in reference to FIG. 1 , as used in this disclosure,“interaction data” from a plurality of accessory devices is datarelating to the activation state and function of the plurality ofaccessory devices 108 and how a subject has changed and/or selected theactivate state and function of the plurality of accessory devices 108.As used in this disclosure, an “activation state,” is a state ofactivation of a device, such as its power status (‘on’ and/or ‘off’),its mode of activation (‘high’, ‘medium’, ‘low’, etc.), and the like.For instance and without limitation, the activation state of athermostat may be ‘cool’, ‘on’, and ‘69 degrees Fahrenheit’, wherein theactivation state includes the current status (on) magnitude (69 degreesFahrenheit) and direction (cool) of the thermostat's currentfunctioning. As used in this disclosure, the “function of the pluralityof accessory devices,” is the purpose, action, or effect of theplurality of accessory devices. For instance and without limitation, thefunction of a thermostat may include ‘the ability to maintain adesirable temperature of an area’, ‘cooling’, and/or ‘heating’. Thefunction ‘as it relates to the presence of the subject’, for instanceand without limitation, may refer to an accessory device 108 such as athermostat in a particular activation state, such as initiating acooling function, due to the subject's willingness to control the deviceby interacting with the device to control the activation state. Innon-limiting illustrative examples, ‘the activation state and functionof the plurality of accessory devices as it relates to the presence ofthe subject’ may be data received by computing device 104 that relatesto whether a subject is comfortable, tired, restless, hot, cold, hungry,or the like, according to how the subject interacts with an accessorydevice 108 and/or how the computing device has received data about thesubject, for instance as retrieving a biometric profile, receivingwearable device data, and the like, as described in further detailbelow.

Continuing in reference to FIG. 1 , computing device 104 is configuredfor receiving a plurality of signals from at least a sensor proximate tothe subject. Each sensor of a plurality of sensors may be configured todetect within the proximity of the subject, wherein ‘proximity’ mayrefer to being in contact with a subject's skin, body, ingested,injected, and/or placed inside a subject, in the same room as a subject,directed towards a subject, or configured in any way to collect signalsfrom subject. Each sensor 124 may generate a plurality of signalscorresponding to the type of data the sensor 124 is configured tocollect; the plurality of signals may be stored and/or retrieved from adatabase, as described in further detail below. At least a sensor 124may include a wearable device such as such as an accelerometer,pedometer, gyroscope, electrocardiography (ECG) device,electrooculography (EOG) device, bioimpedance monitor, blood pressureand heart rate monitor, respiration monitor, force sensor, oxygenationmonitor, biosensors, UV sensor, thermal sensor, fitness trackers, forcemonitors, motion sensors, audio-visual capture data, social mediaplatform data, and the like. At least a sensor 124 may include wearablesthat are capable of transmitting a signal pertaining to a subjectduring, for instance and without limitation, fitness activity using avariety of methods, for instance with without limitation, by utilizingwearable adhesive sensors that attach to the skin, silver/silverchloride traces in compression garments, textile band electrodes, andthe like, as a wearable sensor. At least a sensor 124 may containflexible and/or disposable-type sensors that are applied in a mannersimilar to how medical devices for monitoring athletes in sportsphysiology studies, diabetes monitoring, heart monitoring byelectrocardiogram (ECG/EKG), and the like.

Continuing in reference to FIG. 1 , a signal from at least a sensor 124may include signals from a wearable fitness device. As used in thisdisclosure, “signal” is a signal containing data indicative of asubject's physiological state; physiological state may be evaluated bythe computing device 104 with regard to one or more measures of healthof a subject's body, one or more systems within a subject's body such asa circulatory system, a digestive system, a nervous system, or the like,one or more organs within a subject's body, and/or any other subdivisionof a subject's body useful for diagnostic or prognostic purposes. Atleast a sensor 124 signal may include unique biometric data such asretina scan data, fingerprint data, voice recognition data, facialrecognition data, and the like. For instance, and without limitation,biometric profile 112 may include sensor 124 data directed to aparticular set of biomarkers, test results, and/or biochemicalinformation which may be recognized in a given medical field as usefulfor identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,sensor 124 data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, electrolytes, AST and ALT content, bloodglucose, CK levels, and/or mean corpuscular hemoglobin concentration maybe recognized as useful for identifying various conditions such asdehydration, high and/or low testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, overtraining, blood loss,and/or acute injury.

With continued reference to FIG. 1 , sensor 124 signal data may include,without limitation, hematological data, such as red blood cell count,which may include a total number of red blood cells in a person's bloodand/or in a blood sample, hemoglobin levels, hematocrit representing apercentage of blood in a person and/or sample that is composed of redblood cells, mean corpuscular volume, which may be an estimate of theaverage red blood cell size, mean corpuscular hemoglobin, which maymeasure average weight of hemoglobin per red blood cell, meancorpuscular hemoglobin concentration, which may measure an averageconcentration of hemoglobin in red blood cells, platelet count, meanplatelet volume which may measure the average size of platelets, redblood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Sensor signal data mayinclude, without limitation, immune function data such as Interleukine-6(IL-6), TNF-alpha, systemic inflammatory cytokines, and the like.

Continuing to refer to FIG. 1 , sensor 124 signal data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Sensor signal data may include measures of glucose metabolismsuch as fasting glucose levels and/or hemoglobin A1-C (HbA1c) levels.Sensor 124 signal data may include, without limitation, one or moremeasures associated with endocrine function, such as without limitation,quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate, quantitiesof cortisol, ratio of DHEAS to cortisol, quantities of testosteronequantities of estrogen, quantities of growth hormone (GH), insulin-likegrowth factor 1 (IGF-1), quantities of adipokines such as adiponectin,leptin, and/or ghrelin, quantities of somatostatin, progesterone, or thelike. Sensor 124 signal data may include measures of estimatedglomerular filtration rate (eGFR). Sensor 124 signal data may includequantities of C-reactive protein, estradiol, ferritin, folate,homocysteine, prostate-specific Ag, thyroid-stimulating hormone, vitaminD, 25 hydroxy, blood urea nitrogen, creatinine, sodium, potassium,chloride, carbon dioxide, uric acid, albumin, globulin, calcium,phosphorus, alkaline phosphatase, alanine amino transferase, aspartateamino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Sensor 124 signal data may includeantibody levels. Sensor 124 signal data may include data concerningheavy metal and/or toxin levels such as for lead, cadmium, and arsenic.Sensor 124 signal data may include levels of fibrinogen, plasma cystatinC, and/or brain natriuretic peptide. Biometric profile 112 of a subjectmay include the above data and may include determinations aboutdisorders from the above signals, for instance and without limitationimmunological disorders such as Hashimoto's Thyroiditis, Graves'Disease, diabetes, or the like, which may indicate a subject prefers aparticular environment such as a particular temperature.

Continuing to refer to FIG. 1 , sensor 124 signal data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Sensor 124 signaldata may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Sensor 124 signal datamay include a measure of waist circumference. Sensor 124 signal data mayinclude body mass index (BMI). Sensor 124 signal data may include one ormore measures of bone mass and/or density such as dual-energy x-rayabsorptiometry. Sensor 124 signal data may include one or more measuresof muscle mass. Sensor 124 signal data may include one or more measuresof physical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1 , sensor 124 signal data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Sensor signal data mayinclude one or more evaluations of sensory ability, including measuresof audition, vision, olfaction, gustation, vestibular function, andpain. Sensor 124 signal data may correspond to the level of pain and/ordiscomfort a subject may be experiencing.

Continuing to refer to FIG. 1 , sensor 124 signal data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from subjectinteractions with persons, documents, and/or computing devices; forinstance, subject patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

Still referring to FIG. 1 , sensor 124 signal data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Sensor 124 signal data may includeproteomic data, which as used herein, may include data describing allproteins synthesized and/or modified by subject, including anymicrobiome organism, colony of organisms, or system of organisms, and/ora subset thereof. Sensor 124 signal data may include data concerning amicrobiome of a subject, which as used herein, may include any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on other sensorsignal data of a person, and/or on prognostic labels and/or ameliorativeprocesses as described in further detail below.

Still referring to FIG. 1 , sensor 124 signal data may include skin datareceived from a skin analyzing device. “Skin data,” as used herein, isanalytics of a user's skin. For example, skin data may include analyticsregarding the epidermis, dermis, and the hypodermis layers of skin. Skindata may include data regarding oil and sweat glands, nerves, hairfollicles, and other structures related to the skin. Skin data mayinclude analytics regarding the complexion of pores, spots,pigmentation, moisture, fine lines, wrinkles, melanin, tone, keratin,redness, pH level, texture, porphyrins, and UV spots. A skin analyzingdevice may include a camera or camera-based computing system utilizingRGB visible light, PL polarized light, UV spectrum imaging technology,and the like to analyze a user's skin.

Still referring to FIG. 1 , in some embodiments, sensor 124 signal datamay include a discovery center experience. A “discovery centerexperience” as used in this disclosure is defined as a set of simulateddata generated at an online platform, known as a discovery center, whichdescribes various potential health experiences to optimize user'shealth. “Simulated data” as used in this disclosure is defined as takinga large amount of data and using it to mimic real-world scenarios ofconditions. For example, a discovery center experience may include afuture user experience wherein a comprehensive body scan may beperformed, and user may be portrayed in a 3-dimensional version and havean experience to simulate what happens if user chooses optimalnourishment. User may also see what their future self may look like 3,6, 9 or 12 months into the future and the like. A user display may showwhat living optimally looks like as well as what user could look likewith no change and alternatively, what user could look like withoutproper nourishment. A discovery center experience may also include oneor a number of biological extractions from the user. These biologicalextractions might include physical experiences, diagnostic testing,sleep patterns, fitness patterns, current nutrient levels or any otherform of information coming from the user that is then utilized todetermine their optimal nourishment needs. Nourishment needs may includea complete state of physical, mental, nutritional, and spiritualwellbeing. A discovery center experience may include embodiments asdisclosed in U.S. Non-provisional application Ser. No. 17/387,245 filedon Jul. 38, 2021 and entitled “METHODS AND SYSTEMS FOR PROVIDINGALIMENTARY ELEMENTS,” the entirety of which is incorporated herein byreference. With continued reference to FIG. 1 , sensor 124 signal datamay include one or more subject-entered descriptions of a person'sphysiological state, wherein the sensor 124 is a user device such as a“smartphone”, mobile device, or the like, that is configured to acceptinput from subject. One or more subject-entered descriptions mayinclude, without limitation, subject descriptions of symptoms, which mayinclude without limitation current or past physical, psychological,perceptual, and/or neurological symptoms, subject descriptions ofcurrent or past physical, emotional, and/or psychological problemsand/or concerns, subject descriptions of past or current treatments,including therapies, nutritional regimens, exercise regimens,pharmaceuticals or the like, or any other subject-entered data that asubject may provide to a medical professional when seeking treatmentand/or evaluation, and/or in response to medical intake papers,questionnaires, questions from medical professionals, or the like.Sensor 124 signal data may include any sensor signal data, as describedabove, describing any multicellular organism living in or on a personincluding any parasitic and/or symbiotic organisms living in or on thepersons; non-limiting examples may include mites, nematodes, flatworms,or the like. Examples of sensor signal data described in this disclosureare presented for illustrative purposes only and are not meant to beexhaustive.

With continued reference to FIG. 1 , sensor 124 signal may includephysiological data captured as the result of a medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like, wherein the sensor 124 may include a computingdevice 104, user device, or other device used to collect signalspertaining to the test and/or assay. System 100 may receive at least asensor 124 signal from one or more other devices after performance;system 100 may alternatively or additionally perform one or moreassessments and/or tests to obtain at least a sensor 124 signal, and/orone or more portions thereof, on system 100. For instance, sensor 124signal may include or more entries by a subject in a form or similargraphical subject interface object; one or more entries may include,without limitation, subject responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least acomputing device 104 may present to subject a set of assessmentquestions designed or intended to evaluate a current state of mind ofthe subject, a current psychological state of the subject, a personalitytrait of the subject, or the like; at least a computing device 104 mayprovide subject-entered responses to such questions directly as at leasta physiological data and/or may perform one or more calculations orother algorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1 , assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a subject. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1 , sensor 124 data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1 , sensor 124 data may be obtainedfrom a wearable sensor. At least a sensor 124 may include any medicalsensor 124 and/or medical device configured to capture sensor 124 dataconcerning a subject, including any scanning, radiological and/orimaging device such as without limitation x-ray equipment, computerassisted tomography (CAT) scan equipment, positron emission tomography(PET) scan equipment, any form of magnetic resonance imagery (MM)equipment, ultrasound equipment, optical scanning equipment such asphoto-plethysmographic equipment, or the like. At least a sensor 124 mayinclude any electromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor 124 may include a temperature sensor. At least a sensor124 may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate, or the like. At least a sensor 124 may detectbioimpedance of a subject including as it pertains to swelling,inflammation, hydrated state, and the like. At least a sensor 124 maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Atleast a sensor 124 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 124 may be a part ofsystem 100 or may be a separate device in communication with system 100,such as in a wearable fitness device. Persons skilled in the art, uponreview of this disclosure in its entirety, will be aware of the varioustypes of sensors which may collect signals of the physiological datadescribed above.

Continuing in reference to FIG. 1 , retrieving a biometric profile mayinclude generating machine-learning model training data from a pluralityof signals. The plurality of signals received from at least a sensor 124may be used as training data for a machine-learning process, algorithm,and/or generating a machine-learning model.

Continuing in reference to FIG. 1 , computing device 104 may retrieve abiometric profile of the subject. A “biometric profile,” as used in thisdisclosure, is a collection of determinations, qualitative and/orquantitative metrics, and data that relate to the physical status of thesubject with respect to the subject's environment. In non-limitingillustrative examples, a biometric profile 112 may include a subject'sunique environment parameters for optimum sleep. In such an example, thebiometric profile 112 may include data corresponding to a particulartemperature set by a thermostat, humidity controlled by a humidifier,sound level controlled by an ambient sound machine, a certain brightnessset for the television, mobile phone, and light fixtures within aparticular timeframe of attempting to sleep, a particular firmness ofthe sleeping surface such as a bed firmness setting (for instance, aSLEEP NUMBER mattress setting), a particular window of time for sleep,including a particular alarm and alarm settings for waking up on aparticular device, among configuration of other environmental parametersconducive to sleeping for the subject. As used in this disclosure, an“environmental parameter,” is a parameter relating to an aspect of asubject's environment. An environmental parameter may include physicalparameters such as temperature, humidity, air quality, UV index, soundlevel, lighting level, device activation state, physical movement ofitems, among other environmental parameters. An environmental parametermay include non-physical parameters such as the “feeling” and/or“atmosphere” of a room, for instance from the color and amount oflighting, the type of sounds emitted from accessory devices 108, amongother environmental parameters. Environmental parameters may becontrolled and/or manipulated by accessory devices 108.

Continuing in reference to FIG. 1 , retrieving a biometric profile 112may include storing and/or retrieving biometric profile 112 from adatabase, such as a NoSQL database, relational database, or any othersuitable data configuration and storage mechanism, as described infurther detail below. A biometric profile 112 may be retrieved as afunction of signals, as described in further detail below, for instance,computing device 104 may ‘know’ to store and/or retrieve biometricprofile 112, or subsets of associated data, as a function of aclassifier describing sensor 124 signal data.

Continuing in reference to FIG. 1 , computing device 104 retrieving thebiometric profile may include generating a representation of thebiometric profile 112 via a graphical user interface. A “graphical userinterface,” as used in this disclosure, is any form of interface thatallows a subject to interface with an electronic device throughgraphical icons, audio indicators, text-based interface, typed commandlabels, text navigation, and the like, wherein the interface isconfigured to provide information to the subject and accept input fromthe subject. Graphical user interface may accept subject input, whereinsubject input may include communicating with system 100 to initiateaccessory device 108 state changes, input biometric profile 112 data,and the like. Subject input via a graphical user interface may includedeselecting elements in the pattern of accessory device states, asdescribed in further detail below, changing activation thresholds,and/or modifying or altering any other determinations described herein.Subject input via a graphical user interface may include inputtingsatisfaction and/or dissatisfaction with accessory device 108 statechange, optimum environmental parameters, and the like. Persons skill inthe art, upon review of this disclosure in its entirety, will be awareof the various ways in which a graphical user interface may display theinformation herein and the various devices which may be a user device.

Continuing in reference to FIG. 1 , computing device 104 is configuredto identify a pattern of accessory device states for the plurality ofaccessory devices 108. A “pattern of accessory device states,” as usedin this disclosure, is a set of data pertaining to the state of at leastone accessory device, including the identity or identities of the atleast one accessory device, the at least one accessory device'sactivation state, including data corresponding to the current state offunctioning, and the like. Computing device 104 may identify a ‘pattern’of accessory device states, for instance and without limitation, whereinthe computing device 104 identifies a pattern of which accessory devices108 in an area, such as a house, may be currently powered “on”, whattheir functioning state is, how long they have been “on”, whichaccessory devices 108 are “off”, how the accessory devices that are “on”and functioning have an effect on the environment they are positionedin, among other data. In non-limiting illustrative examples, computingdevice 104 may identify a “pattern” wherein the pattern indicated thatceiling fans are “on” and air conditioning is “on” but the thermostat isindicating that the temperature is not decreasing due to windows anddoors being open, including the garage door. In such an example,computing device 104 may identify a pattern which may include that thetotality of accessory devices 112 that are “on”, are functioning to coolthe house, but as indicated by the thermostat are not succeeding likelydue to the windows and doors being ajar. Computing device 104 maydetermine a pattern by receiving the interaction data for the pluralityof accessory device 108, as described above.

Continuing in reference to FIG. 1 , identifying a pattern of accessorydevice states 116 may include determining, by the computing device 104,a coordinated state change for a group of accessory devices of theplurality of accessory devices 108 as a function of the interaction dataand the biometric profile 112. A “coordinated state change,” as used inthis disclosure, is a coordinated change to the activation state and/orfunctioning of an accessory device 108 and/or group of accessory devices108. Computing device 104 may receive interaction data, as describedabove, about a plurality of accessory devices 108 and retrieve abiometric profile 112 pertaining to a subject, and determine acoordinated state change 120 for the plurality of accessory devices 112,wherein the coordinated state change 120 is performed in response tochanging the pattern of accessory device states 116 to a state that moreclosely resembles an optimum environment described by the biometricprofile 112, as described in further detail below.

Continuing in refence to FIG. 1 , computing device 104 may train abiometric machine-learning model with training data that includes aplurality of entries wherein each entry models sensor 124 signals tophysiological data related to biometric state metrics data. As used inthis disclosure, a “biometric state metric,” is an element of data thatdescribes a datum present in the biometric profile 112 of a subject. Abiometric state metric may include an ideal room temperature for asubject with a particular age, medical history, and BMI. A biometricstate metric may include an optimum lighting level in a room for asubject who routinely studies. Biometric machine-learning model may beany machine-learning algorithm that may be performed by amachine-learning module, as described in further detail below. Biometricmachine-learning model 128 may be trained with training data that is aplurality of sensor 124 signals that relate to biometric state metrics,wherein a plurality of biometric state metrics determined by biometricmachine-learning model 128 may be included in a biometric profile 112.In non-limiting illustrative examples, biometric machine-learning model128 may be trained with senor 124 data over a period of months thatillustrates a subject may increase their quality of sleep, indicated byREM cycles, light sleep patterns, deep sleep patterns, and movement inthe night as detected by a plurality of sensors 124. In such an example,the biometric machine-learning model 128 may determine that the subjectcould potentially increase their REM cycle duration and deep sleepduration, while reducing their movement in the night, by sleeping duringa particular range of thermostat function, noise level, humidity, airpurification activity, going to sleep at a certain time, subjecting thesubject to a particular light level prior to sleeping, among otherenvironmental conditions and device function states. In furthernon-limiting illustrative examples, biometric machine-learning model 128may also indicate relationships of sensor 124 data with temporal datathat indicates optimal ranges of time for device functioning in additionto device parameters that may match biometric state metrics derived inthe model.

Continuing in reference to FIG. 1 , computing device 104 may determine,using the biometric machine-learning model, a biometric profile. Thebiometric profile may include biometric state metrics that describe an‘optimum environment’. As used in this disclosure, an “optimumenvironment,” is a collection of environmental parameters that describedan ideal environment, situation, and/or state for a subject. Biometricprofile 112 may include a comprehensive profile of biometric statemetrics that describe optimum environment conditions and cognate deviceconfiguration states for a subject in a variety of situations, forinstance, while exercising, sleeping, eating, studying, working,pursuing leisure activities, and the like. For instance, and withcontinued reference to the examples above, biometric machine-learningmodel 128 may derive relationships, heuristics, patterns, and the like,from the training data that corresponds to environmental conditions andcognate device function states that result in optimum settings for thesubject. “Optimum” may refer to a singular value, state, signifier,descriptor, and/or a range of values, states, signifiers, descriptors,or the like, that describes maximal attainment of a criterion. Forinstance, such a criterion could be maximal subject satisfaction,wherein what is ‘optimum’ is subject to input from subject. Optimum mayinclude a range of values, settings, and the like, that can bedetermined by system 100, for instance by retrieving a biometric profile112. Optimum may refer to a local maxima or local minima observed intraining data, for instance and without limitation, “maximizing REMcycle sleep within an 8-hour period of time,” wherein the REM cycle canonly be maximized to a value ≤8 hours, and the optimum device states maybe local maxima identified in a machine-learning model associated withsuch a time point.

Continuing in reference to FIG. 1 , identifying the pattern of accessorydevice states 116 for the group of accessory devices may includeretrieving a biometric profile 112, wherein the biometric profile 112contains environmental parameters associated with the plurality ofaccessory devices 180. A pattern of changes in the group of accessorydevices may include a pattern that “should be applied” or may improvethe functioning of the accessory devices. Computing device 104 may beconfigured to receive interaction data, as described above, whichdescribes the activation state, functional relevance, and current stateof operation of a plurality of accessory devices 108. Computing device104 may be configured to determine a pattern of changes in at least agroup of accessory devices of the plurality of accessory device 108. Asused herein, a “group of accessory devices,” may include at least oneaccessory device 108 that will undergo a state change, wherein at leastone other device, of the remainder of devices, may undergo a statechange that is “unchanged”. Therefore, a pattern of state change of a“group of accessory devices” may refer to changing a single accessorydevice 108, but the pattern may include the fate of another accessorydevice 108. Computing device 104 may retrieve, or otherwise determine, abiometric profile 112 of a subject to retrieve environmental parametersthat should be achieved by a group of accessory devices within theplurality of accessory devices 108 and identify a pattern of accessorydevices states 116 that represent how the group of accessory devicesshould be changed to more closely resemble optimum environmentparameters described by the biometric profile 112.

Continuing in reference to FIG. 1 , computing device 104 may identify apattern of changes in the group of accessory devices, wherein thepattern of changes is from a first state described in the interactiondata to a second state related to the optimum environmental parameters.Computing device 104 may identify the identity of any accessory devicesin the group of accessory devices to under a state change by, forinstance, by comparing the interaction data (current state of accessorydevices and the function of each accessory device) to the optimumenvironmental parameters for a second state (what the end state ofaccessory devices should be) and determine the identities of whichaccessory devices 108 should undergo a state change and to what statethey will change to, resulting in an output that is a coordinated statechange to the activation state and/or functioning of the group ofaccessory devices. Alternatively or additionally, computing device 104may also determine the identities of the group of the accessory devicesthat will undergo the coordinated state change and the identities andstates of the remainder of the plurality of accessory devices 108 thatwill not undergo a coordinated state change, and may output such adetermination as a pattern of accessory device states 116. Computingdevice 104 may identify a pattern of changes in the group of accessorydevices from the first state described in the interaction data, to asecond state related to the optimum environmental parameters identifiedin a biometric profile 112, for instance as output by biometricmachine-learning model 128.

Continuing in reference to FIG. 1 , determining the biometric profile112 may include using a biometric machine-learning process to generate aclassifier, wherein the classifier contains a subset of data relating tobiometric data. Biometric data may include accessory device 108 statechanges, patterns of accessory device states, biometric-relatedmaladies, and the like. A classifier may be a subset of data describing,for instance the identities of accessory devices 108 and the associatedactivation states that may represent optimum environmental parametersand device configurations for subjects with alike biometric profiles112. A biometric machine-learning process 132 may include anymachine-learning algorithm and/or process performed by using amachine-learning module, as described in further detail below. A“classifier,” as used in this disclosure, is configured to output atleast a datum that labels or otherwise identifies a set of data that areclustered together, as described in further detail below. A classifier136 may represent a body of data that is a series of biometric data froma plurality of subjects associated with accessory devices 108, accessorydevice states, patterns, and the like. In non-limiting illustrativeexamples, a classifier 136 may relate to the identity and states ofaccessory devices 108, optimum environment parameters, and the like,that may be a packet of data used to search or otherwise identifydeterminations by system 100 described herein.

Continuing in reference to FIG. 1 , retrieving the biometric profile 112may include searching for at least a malady as a function of theplurality of data obtained from the at least a sensor 124. A “malady,”as used in this disclosure, is a deviation from a healthy, or otherwisenormal, physiology indicated as a function of the plurality of signals,as described above. A malady 140 may include a pattern of accessorydevice states, activation states, and/or accessory device use patternsderived from the plurality of signals. For instance and withoutlimitation, a malady 140 may be that a subject prefers low light forstudying, reading, electronic device usage, which may indicate strainedvision, contribution to astigmatism, and other vision issues. Innon-limiting illustrative examples, a malady may be that a subjectwildly varies the temperature of their environment through use of thethermostat, space heaters, ceiling fans, and the like, which may beindicative of the subject having trouble regulating body temperature.Determining if a malady exists in a subject may include comparingbiometric state thresholds, for instance a current biometric state to athreshold for healthy individuals. A “biometric state threshold,” asused in this disclosure, is a value, metric, or element of data, thatrepresents a threshold of an environmental parameter that may becontrolled by an accessory device 108. For instance and withoutlimitation, a biometric state threshold may include metrics thatdescribe a pattern of accessory device 108 states that involve theactivation states of a group of accessory devices to keep the air purityabove a specific threshold during the spring and summer season toprevent a subject's allergic reactions. In non-limiting illustrativeexamples, the biometric state threshold may be a pollen count, pollenindex, or the like, as determined by a UV sensor about a certain value,wherein that value is achieved from the activation state of and/orfunction of windows, doors, thermostat, air purifier, humidifier, andthe like. In such an example, the biometric state threshold mayrepresent environmental parameters relating to air purity that theaccessory devices may control to maintain the area about a thresholdvalue of allergens, above which the subject becomes uncomfortable.

Continuing in reference to FIG. 1 , computing device 104 may search,using the data in the classifier 136, for at least a malady 140. Forinstance, a classifier 136 may contain data categorized for comparing asubject's biometric state threshold to thresholds for a subset ofhealthy individuals for determining the presence of a malady 140. Innon-limiting illustrative examples, computing device 104 may thensearch, for instance from a database, for a malady 140 as a function ofwearable device data contained in the classifier 136. In such anexample, computing device 104 may likewise generate instead a new,distinct classifier 136 for the wearable device data as it relates tomaladies 140. Classifier 136 for searching a malady 140 may be generatedby a classification machine-learning process using any classificationalgorithm, as described in this disclosure. Training data for generatingsuch a classifier 136 for may originate from accessory device 108 usage.Accessory device 108 usage may be classified as a function of biometricstate thresholds in a particular subset of subjects. For instance andwithout limitation, accessory device 108 use data in a subject comparedto alike subjects may be helpful in discerning if a subject is using anaccessory device 108 to excess. Excessive accessory device 108 usage maybe indicative of a malady 140. Training data may come from a pluralityof signals collected by at least a sensor 124. Training data may comefrom a device configuration database, as described in further detailbelow, that stores accessory device 108 usage, patterns of usage,coordinated state changes, and the like. Classification machine-learningprocess may accept an input of such training data and generate aclassifier that categories accessory device 108 usage as a function of abiometric state threshold to generate an output that is a classifier 136that may be used to search for a malady 140.

Continuing in reference to FIG. 1 , computing device 104 may use aclassifier 136 to describe biometric state threshold, wherein theclassifier 136 describes a subset of data for healthy, or otherwisenormal, individuals. Alternatively or additionally, such a biometricstate threshold may be generated using a machine-learning process, moregenerally, such as calculating a threshold from biometric profile data(for instance using regression/neural nets). Threshold may be generatedusing any machine-learning algorithm described herein. Training data forclassifier 136 may originate from biometric profile 112 data retrievedby computing device 104 from the subject and/or a plurality of subjects.Training data may also include accessory device 108 identities, usepatterns, and the like. Training data may be generated by subject as afunction of using accessory devices 108. Training data may be generatedfrom a plurality of signals generated by sensor 124, A machine-learningprocess, such as a biometric machine-learning process 132 may generate aclassifier 136 using input data from a subject's biometric profile 112and generate a classifier 136 as an output.

Continuing in reference to FIG. 1 , in non-limiting illustrativeexamples, computing device 104 may use such a classifier 136 describinga subset of alike subject biometric profile 112 data and compare, forinstance, optimum environment parameters, pattern of accessory devicestates 116, and the like, to determine that a subject prefers highertemperatures than others for their age, sex, and the like. For instanceand without limitation, computing device 104 may identify from abiometric profile 112 a subject's preference for an elevated roomtemperature of 74 degrees Fahrenheit. In such an example, computingdevice 104 may compare subject's preference against a determined‘biometric state threshold’, wherein the threshold may indicate anaverage value of 69 degrees Fahrenheit with a standard deviation of 2.0degrees Fahrenheit. Upon comparison of the subject value against thebiometric state threshold, computing device may query for at least amalady that may match a subject's preference for an unusually high roomtemperature. Computing device 104 may perform a query due to thesubject's accessory device patterns indicating a preference for higherroom temperature than a threshold for alike subjects, and locate that asubject may have anemia, thyroid issues, hypothalamic dysfunction,various nutritional deficiencies, and the like. Computing device 104 mayquery via an online web-browser using a textual-based search;additionally, computing device 104 may query a NoSQL database, aresearch repository, or the like.

Continuing in reference to FIG. 1 , computing device 104 may determine,using the malady 140, a biometric-related comorbidity. A “comorbidity,”as used in this disclosure, may include a chronic and/or acute disease,disorder, condition, injury, and/or symptom that may accompany orotherwise be related to a malady 140. A comorbidity may include anobject-addressable malady, wherein the “object-addressable malady” is adisease, disorder, condition, injury, and/or symptom that may beaddressed by the activation of and/or function of an accessory device108. Essentially, system 100 may identify if a subject's pattern ofaccessory device states 116 indicates an abnormality when compared tosubsets of data from alike subjects. For instance and withoutlimitation, determining if a subject's deviation in environmentparameters from alike subjects may represent a malady, comorbidity,condition, or the like, that may be ameliorated, addressed, or otherwisereduced by configuring accessory devices 108 in a particular manner.

Continuing in reference to FIG. 1 , computing device 104 is configuredto determine an automation rule for the group of accessory devices as afunction of the pattern of accessory device states. As used in thisdisclosure, an “automation rule,” is a specification of an action to betaken by one or more accessories and an activation threshold under whichthe action is to be taken. An “activation threshold,” as used in thisdisclosure is a triggering condition for executing an action of theautomation rule. The automation rule 144 action may be any action thatmay be performed by an accessory device 108. The rule may specify whichaccessory device 108 (or plurality of accessory devices 108) is to act.The activation threshold may be any condition that is detectable bycomputing device 104 or by any accessory device 108. For example andwithout limitation, an automation rule 144 may specify that a porchlight (an accessory device 108) is to be activated if an outside ambientlight sensor (which may be a separate accessory device 108 and/or acomponent of the porch light accessory device 108) detects a light levelbelow a threshold, or at a specific time each night (such as 6:30 pm),or at a time determined based on information available to computingdevice 104 (such as sunset, wherein computing device 104 may determinethe time of sunset by accessing weather data via the Internet, or thelike). In further non-limiting examples, an action may include turningon a heating (or cooling) system to adjust the temperature of a house toa target temperature or changing the target temperature for the heating(or cooling) system. In such an example, the activation threshold for atemperature change may be, for example, a specific time (for instance,shortly before the time the subject normally arrives at home) or aspecific event (for instance, when the subject actually arrives home,wishes to retire for the night, plans to begin a home workout routine,etc.).

Continuing in reference to FIG. 1 , automation rules 144 may beestablished in any manner desired. For example and without limitation, asubject may establish an automation rule 144 by direct input, such asvia a graphical user interface by entering explicit instructionsspecifying an activation threshold and the action to be taken inresponse to that condition. In non-limiting illustrative embodiments,computing device 104 or other components of system 100 may ‘learn’ thesubject's behavior and/or patterns of accessory device 108 usage anddefine suggested automation rules 144, for instance, as described above.In non-limiting examples, computing device 104 or other components ofsystem 100 may present a suggested automation rule 144 to the subject,and the subject may accept or decline the suggestion. Computing device104 and/or accessory device 108 may detect a pattern and suggest anautomation rule 144 to implement these actions automatically or when anactivation threshold is met, such as when the subject arrives home. Ifthe subject accepts the suggestion, the new automation rule 144 may beadded to an automation rules 144 data store, such as a NoSQL database,relational database, online repository, cloud-based repository, or thelike. Other techniques for establishing automation rules 144 may also beused. Likewise, subject input for modifying and/or altering automationrule 144 and/or activation thresholds may be stored and/or retrievedfrom database as part of a subject's biometric profile 112. Suchsubject-indicated inputs may be classified by computing device 104 usinga classification machine-learning process, as described herein.

Continuing in reference to FIG. 1 , determining an automation rule 144may include receiving the coordinated state change 120 for a group ofaccessory devices of the plurality of accessory devices and determiningan activation threshold for changing the states of the plurality ofaccessory device. Computing device 104 may determine activationthreshold by retrieving biometric profile 112. Computing device 104 maydetermine activation threshold by retrieving previous activationthresholds from a database, as described in further detail below.Computing device may use an automation machine-learning process todetermine activation thresholds for changing the states of the group ofaccessory devices. Automation machine-learning process 148 may be anymachine-learning algorithm and/or machine-learning process performed bya machine-learning module, as described in further detail below. Innon-limiting illustrative examples, automation machine-learning process148 may accept an input that is a pattern of accessory device states 116and a coordinated state change 120 intended to achieve a particularoptimum environmental parameters, and generate an output that is aplurality of activation thresholds for executing an activation rule 144.In such an example, automation machine-learning process 148 maydetermine activation thresholds corresponding to when to turn on and/oroff a heating or cooling system, for instance, by controlling athermostat, space heater, ceiling fan, or the like, to achieve aspecific room temperature for a subject prior to reaching their home.

Continuing in reference to FIG. 1 , automation machine-learning process148 may rank the activation thresholds. In non-limiting illustrativeexamples, automation machine-learning process 148 may determineactivation thresholds for accessory devices used in preparing a meal andhosting a house party, wherein the activation thresholds relate to theuse of a variety of household appliances that need to be activated withtemporal specificity, wherein a ranking may be necessary. In such anexample, automation machine-learning process 148 may rank, using aranking function, a plurality of determined activation thresholds,wherein ranking is based on a chronological ordering of activationthresholds in an automation rule 144. Thus, automation machine-learningprocess 148 may generate a plurality of chronologically rankedactivation thresholds. In further non-limiting illustrative examples,ranked activation thresholds may include controlling kitchen appliancesin sequence to prepare Hors d'oeuvres, followed closely by controllinglighting, stereo sound, entertainment system, garage door system, andthe like, in a specific ordering as guests arrive. In such an example,the activation threshold may be different and may occur at differenttimes. Essentially, automation machine-learning process 148 maydetermine the plurality of activation thresholds for the plurality ofaccessory devices 108 and manipulate the plurality of accessory devices108 throughout the day as a function of the automation rule 144 itgenerated.

Continuing in reference to FIG. 1 , computing device 104 may generate,as a function of the ranking, the automation rule 144, wherein theautomation rule 144 may include a hierarchy of instructions foractivating at least an accessory device 108. The hierarchy ofinstructions may include ranked activation thresholds, wherein theactivation thresholds contain a chronological ordering for a group ofaccessory devices within a plurality of accessory devices 108 to changetheir activation state. The hierarchy of instructions may includesimple, binary instructions such as “on/off”. The hierarchy ofinstructions may include more specific qualitative and/or quantitativeinstructions such as “high/medium/low/etc.” settings or a numericalvalue such as a temperature, volume, or the like. The hierarchy ofinstructions may include a ranking wherein a first accessory device 108must adopt a specific activation state prior to triggering theactivation threshold of a second accessory device 108.

Continuing in reference to FIG. 1 , determining an automation rule 144may include transmitting the automation rule 144 as a function of theactivation threshold. The automation rule 144 may include an activationsignal for changing the activation state of accessory device 108. An“activation signal,” as used in this disclosure, is a signal directed toan accessory device 108 to change its activation state. Computing device104 may transmit an activation signal as a function of the automationrule 144, for instance as determined by the automation machine-learningprocess 148. Computing device 104 may transmit to an accessory device108 an automation rule 144, wherein the accessory device 108 may storeand/or retrieve the automation rule 144. Accessory devices 108 mayinteract with computing device 104 and/or amongst one another fortransmission of an activation signal, as described in further detailbelow. Computing device 104 may determine to transmit an activationsignal as a function of the automation rule 144, or as a function of theinteraction data from a plurality of accessory devices 108. Computingdevice 104 may determine to transmit an activation signal as a functionof how the automation rule 144 adopts optimum environment parametersdescribed in a biometric profile 116. In such an instance, certainoptimum environment parameters may have been achieved, wherein at leastan instruction in the automation rule 144 may have been “achieved” or beotherwise made moot, wherein computing device 104 would determine to nottransmit an activation signal to an accessory device 108.

Continuing in reference to FIG. 1 , at least an accessory device 108 maybe configured to accept the automation rule 144, as a function of thebiometric profile 112, from the computing device 104. Computing device104 may transmit an automation rule 144 to at least an accessory device108 related to biometric state data in the biometric profile 112. Forinstance and without limitation, computing device 104 may transmit anautomation rule 144 to achieve optimum environmental parametersdescribed in the biometric profile 112, wherein the parameters mayrelate to light, sound, temperature, air quality, humidity, and thelike, that at least an accessory device 108 available to computingdevice 104 may send an automation rule 144 to change the state of theaccessory device 108.

Continuing in reference to FIG. 1 , changing the state of the group ofaccessory devices may include generating, using the computing device, atleast an accessory device token and transmitting, using the computingdevice, the accessory device token to the accessory device 108. An“accessory device token,” as used in this disclosure, is an access tokencontaining security credentials computing device 104 and accessorydevice 108 use to identify one another and communicate. Computing device104 may establish communication with at least an accessory device 108 bygenerating an accessory device token 152. Computing device 104 maygenerate a unique accessory device token 152 for each device and/or mayestablish an accessory device token 152 for all accessory devices 108communicating with computing device 104. Computing device 104 maygenerate and transmit an accessory device token 152 to authenticate withthe plurality of accessory devices 108 and/or for the plurality ofaccessory devices 108 to communicate with a database that storesautomation rules 144. Accessory device token 152 may include anidentifier associated with a logon session, wherein the identifiercontains credentials to initiate communication between computing device104 and accessory device 108. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in accesstokens may be generated and shared among devices.

Continuing in reference to FIG. 1 , an accessory device token 152 mayinclude communication exchange such as a ‘telecommunication handshake’that includes an automated process of communications between two or moredevices, such as computing device 104 and accessory device 108. Atelecommunication handshake includes the exchange of informationestablishing protocols of communication at the start of communicationbefore full communication commences. A telecommunication handshake mayinclude exchanging signals to establish a communication link as well asto agree as to which protocols to implement. A telecommunicationhandshake may include negotiating parameters to be utilized betweensubject accessory device 108 and computing device 104, includinginformation transfer rate, coding alphabet, parity, interrupt procedure,and/or any other protocol or hardware features. A telecommunicationhandshake may include but is not limited to a transmission controlprotocol (TCP), simple mail transfer protocol (SMTP), transport layersecurity (TLS), Wi-Fi protected access (WPA), and the like.

Continuing in reference to FIG. 1 , transmitting the automation rule 144to the accessory device 108 may include sending a radio wave signature,wherein the radio wave signature is unique. A “radio wave signature,” asused in this disclosure, is a radio frequency communication signaldesigned for communication between components in system 100. Radio wavesignature 156 may be transmitted by a network interface, implemented ona hardware component and/or software component. As used in thisdisclosure, “uniqueness” of the radio wave signature 156 identifies thatthe nature of the radio wave signature 156 is transmitted solely as ameans to activate accessory devices 108 intended for use with system100. The uniqueness of the radio wave signature 156 may include that itdoes not interfere with activating extraneous accessory devices 108, andsuch that extraneous signals cannot mimic radio wave signature 156originating from system 100. For instance and without limitation, radiowave signature 156 is unique in that it may not generically activate allaccessory device 104 at the same time; furthermore, the radio wavesignature 156 may not be mimicked by a secondary source, such as asecondary Wi-Fi generated signal, radio frequency signal, or the like.Radio wave signature 156 may include statistically unique signature suchas a globally unique ID (GUID) or universally unique ID (UUID) or thatsystem 100 may maintain a database of IDs which are unique within alocation, across system 100.

Continuing in reference to FIG. 1 , radio wave signature 156 may includeradio frequencies and electromagnetic frequencies between approximately20 kHz and approximately 300 GHz intended for communication betweenelectronic devices, for instance as commonly used between networkinterfaces and local wireless communication. In exemplary embodiments,transmitting radio wave signature 156 may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(for instance, using cellular telephone technology, data networktechnology such as 3G, 4G/LTE, Wi-Fi (IEEE 802.11 family standards), orother mobile communication technologies, or any combination thereof),components for short-range wireless communication (for instance, usingBluetooth and/or Bluetooth LE standards, NFC, etc.), and/or othercomponents. Network interface may provide wired network connectivity(such as Ethernet) in addition to and/or instead of a wirelessinterface. Network interface may be implemented using a combination ofhardware (for instance, driver circuits, antennas,modulators/demodulators, encoders/decoders, and other analog and/ordigital signal processing circuits) and software components. Networkinterface may support multiple communication channels concurrently,using the same transport or different transports, as necessary.

Continuing in reference to FIG. 1 , computing device 104 may transmitradio wave signature 156 to control devices controlled via a touch pad,touch screen, scroll wheel, click wheel, dial, button, switch, keypad,microphone, or the like, as well as accessory devices 108 containingcontrol elements such as a video screen, indicator lights, speakers,headphone jacks, or the like, together with supporting electronics (forinstance, digital-to-analog or analog-to-digital converters, signalprocessors, or the like). A subject may operate accessory devices 108via user interfaces to invoke the functionality of computing device 104automation rule 144 and may view and/or hear output from computingdevice 104 automation rule 144 via transmission to devices compatiblewith a user interface.

Continuing in reference to FIG. 1 , computing device 104 may control thefunction of the accessory device 108 as a function of the automationrule 144. Transmitting the automation rule 144 to the accessory device108 may include controlling the function of the group of accessorydevices as a function of the biometric profile 112 of the subject.Automation rule 144 may be transmitted to at least an accessory device108 to configure accessory device 108 in accordance with environmentalparameters described in biometric profile 112, as described above.

Referring now to FIG. 2 , an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a subject andwritten in a programming language.

Still referring to FIG. 2 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2 ,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailherein. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailherein; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedherein, such as a mathematical model, neural net, or program generatedby a machine learning algorithm known as a “classification algorithm,”as described in further detail herein, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 216 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of accessorydevice 108 states, patterns, maladies, comorbidities, biometric profiles112, and/or other analyzed items and/or phenomena for which a subset oftraining data may be selected.

Still referring to FIG. 2 , machine-learning module 200 may beconfigured to perform a lazy-learning process 220 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 204. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 204 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail herein.

Alternatively or additionally, and with continued reference to FIG. 2 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2 , machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a plurality of sensor 124 signals and remote data as describedabove as inputs, optimum environment parameters, and a ranking functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; ranking function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Ranking function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 204. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process228 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 2 , machine learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2 , machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 2 , models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 204 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 204.

Referring now to FIG. 3 , a non-limiting exemplary embodiment 300 of adevice configuration database 304 is illustrated. Device configurationdatabase 304 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval database such as a NOSQL database, orany other format or structure for use as a database that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Device configuration database 304 mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableand the like. Device configuration database 304 may include a pluralityof data entries and/or records, as described above. Data entries in andevice configuration database 304 may be flagged with or linked to oneor more additional elements of information, which may be reflected indata entry cells and/or in linked tables such as tables related by oneor more indices in a relational database. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistently with this disclosure. Computingdevice 104 may retrieve any determinations, as described herein, fromthe device configuration database 304, such as a pattern of accessorydevice states as a function of the coordinated state change.

Further referring to FIG. 3 , device configuration database 304 mayinclude, without limitation, an accessory device table 308, indicationdata table 312, biometric profile table 316, signals table 320,automation rule table 324, and/or heuristic table 328. Determinations bya machine-learning process, machine-learning model, ranking function,mapping algorithm and/or objection function, may also be stored and/orretrieved from the device configuration database 304, for instance innon-limiting examples a classifier describing a plurality of biologicalextraction 108 as it relates to a plurality of objects, wherein aclassifier is an identifier that denotes a subset of data that containsa heuristic and/or relationship, as may be useful to system 100described herein. As a non-limiting example, device configurationdatabase 304 may organize data according to one or more instructiontables. One or more device configuration database 304 tables may belinked to one another by, for instance in a non-limiting example, commoncolumn values. For instance, a common column between two tables ofdevice configuration database 304 may include an identifier of asubmission, such as a form entry, textual submission, accessory devicetokens, local access addresses, metrics, and the like, for instance asdefined herein; as a result, a search by a computing device 104 may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of data, including types of data,names and/or identifiers of individuals submitting the data, times ofsubmission, and the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which datafrom one or more tables may be linked and/or related to data in one ormore other tables.

Still referring to FIG. 3 , in a non-limiting embodiment, one or moretables of an device configuration database 304 may include, as anon-limiting example, an accessory device table 308, which may includecategorized identifying data, as described above, including accessorydevices 108, accessory device tokens, and the like. One or more tablesmay include indication data table 312, which may include data regardingactivation state, functionality of devices, and the like, for instanceand without limitation, that system 100 may use to retrieve and/orstore. One or more tables may include biometric profile table 316, whichmay include a biometric state metrics, including classifiers, data, andthe like, for instance and without limitation, that system 100 may useto retrieve and/or store optimum environment parameter associated withsubject. One or more tables may include signals table 320, which mayinclude classifiers, physiological data, and the like, as describedabove for instance and without limitation, that system 100 may use toretrieve and/or store biometric state parameters, optimum environmentparameters, and the like, associated with subject. One of more tablesmay include an automation rule table 324, which may include outputs,determinations, variables, and the like, organized into subsets of datafor coordinated state changes 120 associated with pattern of accessorydevice states 116, activation thresholds, rankings, and the like,associated with executing automation rules 144. One or more tables mayinclude, without limitation, a heuristic table 328, which may organizerankings, scores, models, outcomes, functions, numerical values,vectors, matrices, and the like, that represent determinations,optimizations, iterations, variables, and the like, include one or moreinputs describing potential mathematical relationships, as describedherein.

Referring now to FIG. 4 , a non-limiting exemplary embodiment 400 ofbiometric profile 112 biometric state metric for determining an optimumenvironmental parameter is illustrated. Sensor 124 signal may correspondto tracking subject sleep states, as denoted in FIG. 4 top panel. Sleepmay be tracked with a plurality of sensors 124, for instance, motionsensors present on a subject's limps, such as on the wrist and/or ankle,a sphygmomanometer sensor for blood pressure, a microphone sensor 124for tracking snoring, at least a sensor 124 for tracking respiration andrespiratory cycle, among other sensors 124, which may be used foranalyzing sleep quality. Sleep quality may be tracked as a function oftime via the sensor 124 signal data, wherein sleep quality may includedetermining how long a subject is in REM sleep, deep sleep, or the like.Biometric profile 112 may include at least a biometric state metric fordetermining an optimum environmental parameter, for instance thetemperature conducive to improved sleep quality, as depicted in FIG. 4bottom panel. In such an example, the room temperature as a function oftime can be plotted alongside sleep states, where patterns may emerge. Amachine-learning model may be used to determine the precision and recallin pattern recognition of how temperature relates to sleep quality overtime. A thermometer, or similar thermal sensor 124 may track roomtemperature data alongside sleep data, wherein as the temperatureincreases above 70 degrees Fahrenheit, the subject awakens, as trackedby movement, audio pickup, blood pressure, respiratory cycle, and thelike, that may be tracked by sensor 124.

Continuing in reference to FIG. 4 , biometric profile 112 may include aplurality of biometric state metrics for optimum environment parameters,for instance and without limitation with respect to sleep parameters,accessory devices 108 may be utilized to control room temperature;relative humidity; air quality of pollutants, pollen, dander, and otherallergens; sound level and use of ambient sound control; lighting andelectronic device usage, especially prior to sleep; alarm settings forawakening, among other parameters. Biometric profile 112 and theassociated plurality of biometric state metrics for optimum environmentparameters may be utilized to configure device environment to controlaccessory devices 108 to configure the subject's environment. Biometricprofile 112 and the associated plurality of biometric state metrics foroptimum environment parameters may be used to identify a malady 140. Forinstance and without limitation, patterns of accessory device states116, especially when compared to subgroups of alike subjects usingclassifier 136, may include identifying a malady 140 that predicts orotherwise identifies potential health issues in subject.

Referring now to FIG. 5 , a non-limiting exemplary embodiment 500 of anautomation rule 144 being transmitted by computing device 104 isillustrated. Computing device 104 may include a user device, or anyother device that is capable of performing the functions describedherein by computing device 104, including a “smartphone”, mobile phone,laptop, computer tablet, internet-of-things (IOT) device, and the like.Computing device 104 may transmit an automation rule 144 to a pluralityof accessory devices 108. Accessory devices 108 may receive transmittedautomation rule 144, as described above, using Bluetooth, Wi-Fi internetconnectivity, and the like. A plurality of environmental parameters, asdescribed above, may be controlled and/or affected by a plurality ofaccessory devices 108 including for instance and without limitationthermostat for heating and cooling, speakers, surround soundentertainment systems, televisions, radios, lighting, television andmultimedia devices, garage door systems, remote start vehicles, housealarm systems, and the like. In non-limiting illustrative examples, anautomation rule 144 may be a “vacation protocol in winter” wherein theautomation rule 144 has specific instructions for ‘arming house alarmsystem’, ‘closing garage door’, ‘keep room temperature above 60 degreesFahrenheit to avoid pipes freezing but never exceed 65 degreesFahrenheit to avoid unnecessary energy use’, ‘power off all appliances’,among other potential instructions in automation rule 144. Such anautomation rule 144 may include a triggering condition that relates towhen a subject chooses to initiate the automation rule 144 via agraphical user interface, for instance from the subject's vehicle on theway to the airport.

Referring now to FIG. 6 , a non-limiting exemplary embodiment of amethod 600 for configurable device environment. At step 605, computingdevice 104 is configured for receiving interaction data from a pluralityof accessory devices 108, wherein the interaction data corresponds to aninteraction with a subject. Interaction data from a plurality ofaccessory devices may include data relating to the activation state andfunction of the plurality of accessory devices 108 as it relates to thepresence of the subject; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-5 .

Still referring to FIG. 6 , at step 610, computing device 104 isconfigured for retrieving a biometric profile of the subject as afunction of receiving a plurality of signals from at least a sensor.Retrieving the biometric profile 112 may include receiving subject inputvia a graphical user interface. Retrieving the biometric profile 112 mayinclude training a biometric machine-learning model 128 with trainingdata that includes a plurality of entries wherein each entry modelssensor 124 signals to physiological data related to biometric statemetrics data and determining, using the biometric machine-learning model128, the biometric profile 112; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-5 .

Continuing in reference to FIG. 6 , at step 615, computing device 104 isconfigured for identifying a pattern of accessory device states 116 forthe plurality of accessory devices 108, wherein identifying a patternincludes determining, by the computing device 104, a coordinated statechange 120 for a group of accessory devices of the plurality ofaccessory devices 108 that should be applied as a function of theinteraction data and the biometric profile 112 and identifying thepattern of accessory device states 116 as a function of the coordinatedstate change 120. Identifying the pattern of accessory device states 116that should be applied to the group of accessory devices may includeretrieving a biometric profile 112, wherein the biometric profile 112contains environmental parameters associated with the plurality ofaccessory devices 108 and identifying a pattern of changes in the groupof accessory devices, wherein the pattern of changes is from a firststate described in the interaction data to a second state related to theoptimum environmental parameters. Retrieving the biometric profile 112may include searching for at least a malady 140 as a function of theplurality of data obtained from the at least a sensor 124; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5 .

Continuing in reference to FIG. 6 , at step 615, computing device 104identifying a pattern of accessory device states 116 for the pluralityof accessory devices 108 as a function of the subject's biometricprofile 112 may include receiving a plurality of signals from aplurality of sensors 124, wherein each sensor 124 of the plurality ofsensors 124 is configured to detect within the proximity of the subject,training a biometric machine-learning model 128 with training data thatincludes a plurality of entries wherein each entry models sensor 124signals to physiological data related to biometric state metrics data,determining, using the biometric machine-learning model 128, a biometricprofile 112, wherein the biometric profile 112 may include biometricstate metrics that describe an optimum environment, and identifying apattern of changes in the group of accessory devices, wherein thepattern of changes is from a first state described in the interactiondata to a second state related to the optimum environmental parameters.Determining the biometric profile 112 may include using a biometricmachine-learning process 132 to generate a classifier 136, wherein theclassifier 136 contains a subset of data relating to biometric data,searching, using the data in the classifier 136, for at least a malady140, wherein a malady 140 is indicated in at least a biometric statemetric in comparison to a biometric state threshold in healthy subjects,and determining, using the malady 140, a biometric-related comorbidity;this may be implemented, without limitation, as described above inreference to FIGS. 1-5 .

Continuing in reference to FIG. 6 , at step 620, computing device 104 isconfigured for determining an automation rule 144 for the group ofaccessory devices as a function of the pattern of accessory devicestate. Determining an automation rule 144 may include receiving thecoordinated state change for a group of accessory devices of theplurality of accessory devices 108, determining, using an automationmachine-learning process 148, an activation threshold for changing thestates of the plurality of accessory devices 108, and transmitting theautomation rule 144 as a function of the activation threshold. At leastan accessory device 108 is configured to accept the automation rule 144,as a function of the biometric profile 112, from the computing device104. Changing the state of the group of accessory devices may includegenerating, using the computing device 104, at least an accessory devicetoken 152, transmitting, using the computing device 104, the accessorydevice token 152 to the accessory device 108; this may be implemented,without limitation, as described above in reference to FIGS. 1-5 .

Continuing in reference to FIG. 6 , at step 625, computing device 104 isconfigured for transmitting, to the group of accessory devices, theautomation rule 144. Transmitting the automation rule 144 to theaccessory device 108 may include sending a radio wave signature 156,wherein the radio wave signature 156 is unique and controlling thefunction of the accessory device 108 as a function of the automationrule 144; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-5 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for a configurable device environment,the system comprising a computing device, wherein the computing deviceis configured to: receive remote data corresponding to a subject,wherein the remote data further comprises biological extraction data;retrieve a biometric profile of the subject as a function of a pluralityof signals; identify a pattern of accessory device states for theplurality of accessory devices, wherein identifying the pattern furthercomprises: determining, by the computing device, a coordinated statechange for a group of accessory devices of the plurality of accessorydevices as a function of the interaction data and the biometric profile;and identifying the pattern of accessory device states as a function ofthe coordinated state change; and control the function of the pluralityof accessory devices as a function of the pattern of accessory devicestates.
 2. The system of claim 1, wherein receiving the remote datacomprises receiving interaction data from a plurality of accessorydevices.
 3. The system of claim 2, wherein the interaction datacorresponds to an interaction with a subject.
 4. The system of claim 1,wherein receiving the remote data comprises receiving a plurality ofsignals from at least a sensor proximate to the subject, wherein asignal of the plurality of signals comprises skin data.
 5. The system ofclaim 4, wherein a signal of the plurality of signals comprisessimulated data generated at an online platform.
 6. The system of claim1, wherein controlling the function of the plurality of accessorydevices comprises determining an automation rule for the group ofaccessory devices as a function of the pattern of accessory devicestates.
 7. The system of claim 6, wherein controlling the function ofthe plurality of accessory devices further comprises transmitting, tothe group of accessory devices, the automation rule.
 8. The system ofclaim 1, wherein retrieving the biometric profile further comprises:training a biometric machine-learning model as a function of trainingdata that includes a plurality of entries wherein each entry modelssensor signals to physiological data related to biometric state metricsdata; and determining, as a function of the biometric machine-learningmodel and the plurality of signals, the biometric profile.
 9. The systemof claim 1, wherein: the biometric profile includes environmentalparameters associated with the plurality of accessory devices; andidentifying the pattern of accessory device states that should beapplied to the group of accessory devices further comprises identifyinga pattern of changes in the group of accessory devices, wherein thepattern of changes is from a first state described in the interactiondata to a second state related to the optimum environmental parameters.10. The system of claim 1, wherein retrieving the biometric profilefurther comprises identifying a malady as a function of the plurality ofsignals.
 11. A method for a configurable device environment, the method:receiving, by at least a computing device, remote data corresponding toa subject, wherein the remote data further comprises biologicalextraction data; retrieving, by the at least computing device, abiometric profile of the subject as a function of the plurality ofsignals; identifying, by the at least computing device, a pattern ofaccessory device states for the plurality of accessory devices, whereinidentifying the pattern further comprises: determining, by the computingdevice, a coordinated state change for a group of accessory devices ofthe plurality of accessory devices as a function of the interaction dataand the biometric profile; and identifying the pattern of accessorydevice states as a function of the coordinated state change; andcontrolling, by the at least computing device, the function of theplurality of accessory devices as a function of the pattern of accessorydevice states.
 12. The method of claim 11, wherein receiving the remotedata comprises receiving interaction data from a plurality of accessorydevices.
 13. The method of claim 12, wherein the interaction datacorresponds to an interaction with a subject.
 14. The method of claim11, wherein receiving the remote data comprises receiving a plurality ofsignals from at least a sensor proximate to the subject, wherein asignal of the plurality of signals comprises skin data.
 15. The methodof claim 14, wherein a signal of the plurality of signals comprisessimulated data generated at an online platform.
 16. The method of claim11, wherein controlling the function of the plurality of accessorydevices comprises determining an automation rule for the group ofaccessory devices as a function of the pattern of accessory devicestates.
 17. The method of claim 16, wherein controlling the function ofthe plurality of accessory devices further comprises transmitting, tothe group of accessory devices, the automation rule.
 18. The method ofclaim 11, wherein retrieving the biometric profile further comprises:training a biometric machine-learning model as a function of trainingdata that includes a plurality of entries wherein each entry modelssensor signals to physiological data related to biometric state metricsdata; and determining, as a function of the biometric machine-learningmodel and the plurality of signals, the biometric profile.
 19. Themethod of claim 11, wherein: the biometric profile includesenvironmental parameters associated with the plurality of accessorydevices; and identifying the pattern of accessory device states thatshould be applied to the group of accessory devices further comprisesidentifying a pattern of changes in the group of accessory devices,wherein the pattern of changes is from a first state described in theinteraction data to a second state related to the optimum environmentalparameters.
 20. The method of claim 11, wherein retrieving the biometricprofile further comprises identifying a malady as a function of theplurality of signals.